GPU-Accelerated PD-IPM for Real-Time Model Predictive Control in Integrated Missile Guidance and Control Systems

Sanghyeon Lee; Heoncheol Lee; Yunyoung Kim; Jaehyun Kim; Wonseok Choi

doi:10.3390/s22124512

GPU-Accelerated PD-IPM for Real-Time Model Predictive Control in Integrated Missile Guidance and Control Systems

Sensors (Basel). 2022 Jun 14;22(12):4512. doi: 10.3390/s22124512.

Authors

Sanghyeon Lee¹, Heoncheol Lee², Yunyoung Kim³, Jaehyun Kim³, Wonseok Choi³

Affiliations

¹ Research Institute of Manufacturing and Productivity, Kumoh National Institute of Technology, Gumi 39177, Gyeongbuk, Korea.
² Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Gyeongbuk, Korea.
³ Precision Guided Munition R&D Laboratory, LIGNEX1, Seongnam 13488, Gyeonggi, Korea.

Abstract

This paper addresses the problem of real-time model predictive control (MPC) in the integrated guidance and control (IGC) of missile systems. When the primal-dual interior point method (PD-IPM), which is a convex optimization method, is used as an optimization solution for the MPC, the real-time performance of PD-IPM degenerates due to the elevated computation time in checking the Karush-Kuhn-Tucker (KKT) conditions in PD-IPM. This paper proposes a graphics processing unit (GPU)-based method to parallelize and accelerate PD-IPM for real-time MPC. The real-time performance of the proposed method was tested and analyzed on a widely-used embedded system. The comparison results with the conventional PD-IPM and other methods showed that the proposed method improved the real-time performance by reducing the computation time significantly.

Keywords: graphics processing unit; integrated missile guidance and control; model predictive control; primal-dual interior point method; real-time systems.

MeSH terms

Algorithms*

Grants and funding

This work was supported by Theatre Defense Research Center funded by Defense Acquisition Program Administration under Grant UD200043CD.